# Copyright 2020-2024 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""test checking for some ops"""
import functools
import logging
import pytest
import numpy as np
import mindspore.context as context
from mindspore import Tensor
from mindspore import nn
from mindspore.ops import operations as P
from ..ut_filter import non_graph_engine
from ....mindspore_test_framework.mindspore_test import mindspore_test
from ....mindspore_test_framework.pipeline.forward.compile_forward \
    import pipeline_for_compile_forward_ge_graph_for_case_by_case_config
from ....mindspore_test_framework.pipeline.forward.verify_exception \
    import pipeline_for_verify_exception_for_case_by_case_config

logging.basicConfig(level=logging.WARNING)


# pylint: disable=abstract-method
class NetMissConstruct(nn.Cell):
    """ NetMissConstruct definition """

    def __init__(self):
        super(NetMissConstruct, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5, pad_mode='valid')
        self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
        self.fc1 = nn.Dense(16 * 5 * 5, 120)
        self.fc2 = nn.Dense(120, 84)
        self.fc3 = nn.Dense(84, 10)
        self.relu = nn.ReLU()
        self.max_pool2d = nn.MaxPool2d(kernel_size=2)
        self.flatten = P.Flatten()

    # TestCase: Mis-spelled 'construct' to 'construtc'
    def construtc(self, x):
        x = self.max_pool2d(self.relu(self.conv1(x)))
        x = self.max_pool2d(self.relu(self.conv2(x)))
        x = self.flatten(x)
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x


def test_net_without_construct():
    """ test_net_without_construct """
    net = NetMissConstruct()
    inp = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32))
    with pytest.raises(AttributeError):
        net(inp)


class NetAddN(nn.Cell):
    """net for test AddN"""

    def __init__(self):
        super(NetAddN, self).__init__()
        self.net = P.AddN()

    def construct(self, x):
        return self.net(x)


class NetSplit(nn.Cell):
    "net for test Split"

    def __init__(self):
        super(NetSplit, self).__init__()
        self.net = P.Split(1, 2)

    def construct(self, x):
        return self.net(x)


class NetBatchMatMul(nn.Cell):
    """net for test BatchMatMul"""

    def __init__(self):
        super(NetBatchMatMul, self).__init__()
        self.op = P.BatchMatMul()

    def construct(self, x, y):
        return self.op(x, y)


test_case_check_ops = [
    ('Conv_Padding_1', {
        'block': nn.Conv2d(1, 6, 5, pad_mode='same', padding=0),
        'desc_inputs': [Tensor(np.ones(shape=[1, 1, 6, 5]).astype(np.float32))]}),
    ('Conv_Padding_2', {
        'block': nn.Conv2d(1, 6, 5, pad_mode='valid', padding=0),
        'desc_inputs': [Tensor(np.ones(shape=[1, 1, 6, 5]).astype(np.float32))]}),
    ('Conv_Padding_3', {
        'block': nn.Conv2d(1, 6, 5, pad_mode='pad', padding=0),
        'desc_inputs': [Tensor(np.ones(shape=[1, 1, 6, 5]).astype(np.float32))]}),
    ('Conv_Padding_4', {
        'block': nn.Conv2d(1, 6, 5, pad_mode='pad', padding=7),
        'desc_inputs': [Tensor(np.ones(shape=[1, 1, 6, 5]).astype(np.float32))]}),
    ('Conv_Bias_1', {
        'block': nn.Conv2d(1, 6, 5, has_bias=True, bias_init=Tensor(np.ones([6]).astype(np.float32))),
        'desc_inputs': [Tensor(np.ones(shape=[1, 1, 6, 5]).astype(np.float32))]}),
    ('Conv_Bias_2', {
        'block': nn.Conv2d(1, 6, 5, has_bias=True, bias_init='zeros'),
        'desc_inputs': [Tensor(np.ones(shape=[1, 1, 6, 5]).astype(np.float32))]}),
    ('Conv_Bias_3', {
        'block': nn.Conv2d(1, 6, 5, has_bias=False, bias_init='zeros'),
        'desc_inputs': [Tensor(np.ones(shape=[1, 1, 6, 5]).astype(np.float32))]}),
    ('Conv_Bias_4', {
        'block': nn.Conv2d(1, 6, 5, has_bias=False, bias_init=Tensor(np.ones([6]).astype(np.float32))),
        'desc_inputs': [Tensor(np.ones(shape=[1, 1, 6, 5]).astype(np.float32))]}),
    ('Dense_Bias_1', {
        'block': nn.Dense(1, 6, has_bias=True, bias_init=Tensor(np.ones([6]).astype(np.float32))),
        'desc_inputs': [Tensor(np.ones(shape=[6, 1]).astype(np.float32))]}),
    ('Dense_Bias_2', {
        'block': nn.Dense(1, 6, has_bias=True, bias_init='zeros'),
        'desc_inputs': [Tensor(np.ones(shape=[6, 1]).astype(np.float32))]}),
    ('Dense_Bias_3', {
        'block': nn.Dense(1, 6, has_bias=False, bias_init='zeros'),
        'desc_inputs': [Tensor(np.ones(shape=[6, 1]).astype(np.float32))]}),
    ('Dense_Bias_4', {
        'block': nn.Dense(1, 6, has_bias=False, bias_init=Tensor(np.ones([6]).astype(np.float32))),
        'desc_inputs': [Tensor(np.ones(shape=[6, 1]).astype(np.float32))]}),
    ('MaxPool2d_1', {
        'block': nn.MaxPool2d(5, pad_mode='same'),
        'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}),
    ('MaxPool2d_2', {
        'block': nn.MaxPool2d(5, pad_mode='valid'),
        'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}),
    ('AvgPool2d_1', {
        'block': nn.AvgPool2d(5, pad_mode='same'),
        'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}),
    ('AvgPool2d_2', {
        'block': nn.AvgPool2d(5, pad_mode='valid'),
        'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}),
    ('Conv2D_1', {
        'block': P.Conv2D(1, 6, pad_mode='same', pad=0),
        'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32)),
                        Tensor(np.ones(shape=[1, 5, 6, 6]).astype(np.float32))]}),
    ('Conv2D_2', {
        'block': P.Conv2D(1, 6, pad_mode='valid', pad=0),
        'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32)),
                        Tensor(np.ones(shape=[1, 5, 6, 6]).astype(np.float32))]}),
    ('Conv2D_3', {
        'block': P.Conv2D(1, 6, pad_mode='pad', pad=0),
        'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32)),
                        Tensor(np.ones(shape=[1, 5, 6, 6]).astype(np.float32))]}),
    ('Conv2D_4', {
        'block': P.Conv2D(1, 6, pad_mode='pad', pad=7),
        'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32)),
                        Tensor(np.ones(shape=[1, 5, 6, 6]).astype(np.float32))]}),
    ('MatMul_1', {
        'block': P.MatMul(),
        'desc_inputs': [Tensor(np.ones(shape=[1, 3])), Tensor(np.ones(shape=[3, 4]))]}),
    ('MatMul_2', {
        'block': P.BatchMatMul(),
        'desc_inputs': [Tensor(np.ones(shape=[5, 1, 5])), Tensor(np.ones(shape=[5, 5, 4]))]}),
    ('MatMul_Transpose_1', {
        'block': P.MatMul(transpose_a=True),
        'desc_inputs': [Tensor(np.ones(shape=[3, 1])), Tensor(np.ones(shape=[3, 4]))]}),
    ('MatMul_Transpose_2', {
        'block': P.MatMul(transpose_b=True),
        'desc_inputs': [Tensor(np.ones(shape=[3, 2])), Tensor(np.ones(shape=[5, 2]))]}),
    ('MatMul_Transpose_3', {
        'block': P.MatMul(transpose_a=True, transpose_b=True),
        'desc_inputs': [Tensor(np.ones(shape=[3, 2])), Tensor(np.ones(shape=[5, 3]))]}),
    ('BatchMatMul', {
        'block': NetBatchMatMul(),
        'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5])), Tensor(np.ones(shape=[3, 5, 4]))]}),
    ('BatchMatMul_broadcast_1', {
        'block': NetBatchMatMul(),
        'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5])), Tensor(np.ones(shape=[5, 4]))]}),
    ('BatchMatMul_broadcast_2', {
        'block': NetBatchMatMul(),
        'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5])), Tensor(np.ones(shape=[1, 5, 4]))]}),
    ('BatchMatMul_broadcast_3', {
        'block': NetBatchMatMul(),
        'desc_inputs': [Tensor(np.ones(shape=[2, 1, 1, 5])), Tensor(np.ones(shape=[1, 2, 5, 4]))]}),
    ('BatchMatMul_broadcast_4', {
        'block': NetBatchMatMul(),
        'desc_inputs': [Tensor(np.ones(shape=[2, 2, 1, 1, 5])), Tensor(np.ones(shape=[1, 2, 5, 4]))]}),
    ('BatchMatMul_broadcast_5', {
        'block': NetBatchMatMul(),
        'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5])), Tensor(np.ones(shape=[1, 3, 5, 4]))]}),
]

test_case_lists = [test_case_check_ops]
test_exec_case = functools.reduce(lambda x, y: x + y, test_case_lists)
# use -k to select certain testcast
# pytest tests/python/ops/test_ops.py::test_backward -k LayerNorm



@non_graph_engine
@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config)
def test_exec():
    context.set_context(mode=context.GRAPH_MODE)
    return test_exec_case


raise_set = [
    ('Conv_Padding_1_Error', {
        'block': (lambda x: nn.Conv2d(1, 6, 5, pad_mode='same', padding=7), {'exception': ValueError}),
        'desc_inputs': [Tensor(np.ones(shape=[1, 1, 6, 5]).astype(np.float32))]}),
    ('Conv_Padding_2_Error', {
        'block': (lambda x: nn.Conv2d(1, 6, 5, pad_mode='same', padding=7), {'exception': ValueError}),
        'desc_inputs': [Tensor(np.ones(shape=[1, 1, 6, 5]).astype(np.float32))]}),
    ('Conv2D_1_Error', {
        'block': (lambda x, y: P.Conv2D(1, 6, pad_mode='same', pad=7), {'exception': ValueError}),
        'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32)),
                        Tensor(np.ones(shape=[1, 5, 6, 6]).astype(np.float32))]}),
    ('Conv2D_2_Error', {
        'block': (lambda x, y: P.Conv2D(1, 6, pad_mode='valid', pad=7), {'exception': ValueError}),
        'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32)),
                        Tensor(np.ones(shape=[1, 5, 6, 6]).astype(np.float32))]}),
    ('NetAddN_Error', {
        'block': (NetAddN(), {'exception': TypeError}),
        'desc_inputs': [(np.random.randn(1, 2, 3, 4).astype(np.float32),
                         np.random.randn(1, 2, 3, 4).astype(np.float32))]}),
    ('AddN_Error', {
        'block': (P.AddN(), {'exception': TypeError}),
        'desc_inputs': [(np.random.randn(1, 2, 3, 4).astype(np.float32),
                         np.random.randn(1, 2, 3, 4).astype(np.float32))]}),
    ('Splite_Error', {
        'block': (NetSplit(), {'exception': TypeError}),
        'desc_inputs': [None]}),
    ('MatMul_1_Error', {
        'block': (P.MatMul(), {'exception': ValueError}),
        'desc_inputs': [Tensor(np.ones(shape=[5])), Tensor(np.ones(shape=[4]))]}),
    ('MatMul_2_Error', {
        'block': (P.MatMul(), {'exception': ValueError}),
        'desc_inputs': [Tensor(np.ones(shape=[1, 5])), Tensor(np.ones(shape=[3, 4]))]}),
    ('MatMul_3_Error', {
        'block': (P.MatMul(), {'exception': ValueError}),
        'desc_inputs': [Tensor(np.ones(shape=[1, 5])), Tensor(np.ones(shape=[5, 5, 4]))]}),
    ('MatMul_Transpose_1_Error', {
        'block': (P.MatMul(transpose_a=True), {'exception': ValueError}),
        'desc_inputs': [Tensor(np.ones(shape=[1, 3])), Tensor(np.ones(shape=[3, 4]))]}),
    ('MatMul_Transpose_2_Error', {
        'block': (P.MatMul(transpose_b=True), {'exception': ValueError}),
        'desc_inputs': [Tensor(np.ones(shape=[3, 2])), Tensor(np.ones(shape=[2, 5]))]}),
    ('MatMul_Transpose_3_Error', {
        'block': (P.MatMul(transpose_a=True, transpose_b=True), {'exception': ValueError}),
        'desc_inputs': [Tensor(np.ones(shape=[3, 2])), Tensor(np.ones(shape=[3, 5]))]}),
    ('BatchMatMul_1_Error', {
        'block': (P.BatchMatMul(), {'exception': ValueError}),
        'desc_inputs': [Tensor(np.ones(shape=[5])), Tensor(np.ones(shape=[4]))]}),
    ('BatchMatMul_2_Error', {
        'block': (P.BatchMatMul(), {'exception': ValueError}),
        'desc_inputs': [Tensor(np.ones(shape=[1, 5])), Tensor(np.ones(shape=[3, 4]))]}),
    ('BatchMatMul_3_Error', {
        'block': (P.BatchMatMul(), {'exception': ValueError}),
        'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5])), Tensor(np.ones(shape=[3, 3, 4]))]}),
    ('BatchMatMul_4_Error', {
        'block': (P.BatchMatMul(), {'exception': ValueError}),
        'desc_inputs': [Tensor(np.ones(shape=[3, 1, 5])), Tensor(np.ones(shape=[2, 5, 4]))]}),
]


@mindspore_test(pipeline_for_verify_exception_for_case_by_case_config)
def test_check_exception():
    return raise_set
